I have a R script to load multiple text files in a directory and save the data as compressed .rda. It looks like this,
#!/usr/bin/Rscript --vanilla
args <- commandArgs(TRUE)
## arg[1] is the folder name
outname <- paste(args[1], ".rda", sep="")
files <- list.files(path=args[1], pattern=".txt", full=TRUE)
tmp <- list()
if(file.exists(outname)){
message("found ", outname)
load(outname)
tmp <- get(args[1]) # previously read stuff
files <- setdiff(files, names(tmp))
}
if(is.null(files))
message("no new files") else {
## read the files into a list of matrices
results <- plyr::llply(files, read.table, .progress="text")
names(results) <- files
assign(args[1], c(tmp, results))
message("now saving... ", args[1])
save(list=args[1], file=outname)
}
message("all done!")
The files are quite large (15Mb each, 50 of them typically), so running this script takes up to a few minutes typically, a substantial part of which is taken writing the .rda results.
I often update the directory with new data files, therefore I would like to append them to the previously saved and compressed data. This is what I do above by checking if there's already an output file with that name. The last step is still pretty slow, saving the .rda file.
Is there a smarter way to go about this in some package, keeping a trace of which files have been read, and saving this faster?
I saw that knitr
uses tools:::makeLazyLoadDB
to save its cached computations, but this function is not documented so I'm not sure where it makes sense to use it.
For intermediate files that I need to read (or write) often, I use
save (..., compress = FALSE)
which speeds up things considerably.
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